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arXiv 2020年

作者： Wang, Guangming Yang, Yehui Zhang, Huixin Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Computer Science and Technology University of Cambridge United Kingdom

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution kernel to extract features from raw 3D point clouds because of the unstructured property of point clouds. In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. This newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. In addition, this paper analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Sensitivity analysis of digital filters using the continued fraction expansion 1

Sensitivity analysis of digital filters using the continued ...

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1st Moscow Workshop on Electronic and Networking Technologies, MWENT 2018

作者： Lesnikov, V. Naumovich, T. Chastikov, A. Department of Radioelectronic Means Faculty of Automation and Computer Engineering Institute of Mathematics and Information Systems Vyatka State University Moscow str. 36 Kirov610000 Russia

ISBN: (纸本)9781538634974

It is known that many structural schemes can be used to implement digital filters. Some structures of digital filters are characterized by low sensitivity of the characteristics to the accuracy of the representation of the coefficients. A separate group of methods for the structural synthesis of digital filters is based on the expansion of rational fractions, including transfer functions, into continued fractions. Because of this decomposition, ladder structures are obtained. The sensitivity of such ladder structures is analyzed in this paper. As a measure of the structure sensitivity, estimations of the partial derivatives of the transfer function coefficients with respect to the expansion coefficients in continued fraction were used. The analysis showed that the sensitivity of the transfer function coefficients to some expansion coefficients is very small. However, the presented examples show that the sensitivity to certain coefficients of expansion can exceed the sensitivity of the classical direct and the canonic forms. © 2018 IEEE.

关键词： IIR filters

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Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking 31

Learning Attentions: Residual Attentional Siamese Network fo...

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31st IEEE/CVF Conference on computer Vision and Pattern Recognition (CVPR)

作者： Wang, Qiang Teng, Zhu Xing, Junliang Gao, Jin Hu, Weiming Maybank, Stephen University of Chinese Academy of Sciences Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Department of Computer Science and Information Systems Birkbeck College University of London UK

ISBN: (纸本)9781538664209

Offline training for object tracking has recently shown great potentials in balancing tracking accuracy and speed. However, it is still difficult to adapt an offline trained model to a target tracked online. This work presents a Residual Attentional Siamese Network (RASNet) for high performance object tracking. The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online. In particular, by exploiting the offline trained general attention, the target adapted residual attention, and the channel favored feature attention, the RASNet not only mitigates the over-fitting problem in deep network training, but also enhances its discriminative capacity and adaptability due to the separation of representation learning and discriminator learning. The proposed deep architecture is trained from end to end and takes full advantage of the rich spatial temporal information to achieve robust visual tracking. Experimental results on two latest benchmarks, OTB-2015 and VOT2017, show that the RASNet tracker has the state-of-the-art tracking accuracy while runs at more than 80 frames per second.

关键词： Target tracking Correlation Feature extraction Training Adaptation models Visualization Object tracking

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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On the peculiarities of the exchange of data between applications in high-level languages and matlab functions 1

On the peculiarities of the exchange of data between applica...

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1st Workshop computer Science and Engineering in the framework of the 5th International Scientific-Methodical Conference "Problems of Mathematical and Natural-Scientific Training in Engineering Education, CSITinMNSTinEE 2018

作者： Krasnovidov, Alexander V. Khomonenko, Anatoly D. Zabrodin, Andrew V. Smirnov, Alexander V. Department of Information and Computer Systems Emperor Alexander I St. Petersburg State Transport University St. Petersburg Russia Laboratory of Computer Aided Integrated Systems St.Petersburg Institute for Informatics and Automation of the RAS Saint Petersburg Russia

The possibilities of data exchange between functions in the language of the Matlab system with applications in high-level languages are considered. Programs in high-level languages appropriately to use in conjunction with mathematical packages to collect data, control various technological processes, and perform other similar actions. The instrumental means of organizing such an exchange are characterized. It had been shown the feasibility of integrating applications in high-level languages and the M-function of the Matlab system, if necessary, to organize input / output of large amounts of data. Copyright © by the papers’ authors.

关键词： Electronic data interchange

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The formalization of the characteristics of automatic workplace (AWP) with the use of linguistic variables (LV)

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IOP Conference Series: Materials Science and Engineering 2021年第1期1029卷

作者： S O Kramarov V V Khramov A V Belyaev E V Grebenyuk Institute of Economics and Management Surgut State University Surgut Khanty-Mansi Autonomous Okrug–Ugra Russia Institute of Information Systems Department of Information Systems and Applied Mathematics Southern University (IMBL) Rostov-on-Don Russia Faculty Transportation Service and Operation Department of Radio Electronics Don State Technical University Rostov-on-Don Russia Polytechnic Institute Automation and Computer Systems Department Surgut State University Surgut Khanty-Mansi Autonomous Okrug – Ugra Russia

The article suggests using a new approach to formalize requirements and describe them in the form of a fuzzy area in the settings space. To do this, an alphabet is made that contains the values of linguistic variables. Using this method, it is possible to quantify the extent to which the proposed system is part of a variety of effective ARM's. In this article, the authors used a new solution to apply elements of artificial intelligence and elements of fuzzy logic in the creation of ARM. Using linguistic variables in the article it is possible to determine the degree of belonging of the research system to the effectiveness of this ARM. The method of fuzzy aggregation using linguistic variables (LV) is used in this paper when forming a vector of requirements for an automatic workplace (AWP), as well as its elements, including cognitive analysis methods, a technical description of existing AWPs, their algorithmic and software tools, as well as to determine the degree of compliance of AWP variants with the necessary technical requirements. In this article, we have applied a new approach to formalizing the requirements for determining the effectiveness of ARM use using artificial intelligence and fuzzy logic elements. To do this, we propose to make a special alphabet that contains the values of linguistic variables. Using this method, it is possible to quantify the extent of belonging to many effective ARM's.

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CBS-GAN: A Band Selection Based Generative Adversarial Net for Hyperspectral Sample Generation

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IOP Conference Series: Earth and Environmental Science 2021年第1期734卷

作者： Yulin Qiao Mostofa Zaman Mohammad Yi Li Xiaobo Liu Zhihua Cai School of Automation China University of Geosciences (Wuhan) 430074 Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems 430074 Wuhan China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan 430078 China School of Computer Science China University of Geosciences (Wuhan) 430078 Wuhan China

Sample generation is an effective method to improve the performance of hyperspectral image classification by generating virtual samples for training sample expansion in the training process of classification. However, there are some defects existing in the previous sample generation methods including the lack of spatial information, the redundant generation and the damage of the original spectral components. In this paper, we propose conditional band selection generative adversarial net, named CBS-GAN, to handle this problem. Firstly, the band selection net of CBS-GAN is utilized to avoid redundant bands and keep original spectral information, then the generation net of CBS-GAN generates the spatial-spectral data blocks by selected bands for sample generation. The experiments of classification are also used to demonstrate the availability of virtual samples generated by our method.

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Research on CO 2 Detection System in Refrigerated Compartment of Agricultural Products Based on TDLAS Technology

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Procedia CIRP 2019年 83卷 429-433页

作者： Rui Zhang MaoNing Guo YiZhong Wang HeXuan Yang Bin Hu HongFei Guo Yan Wang College of Electronic Information and Automation Tianjin University of Science & Technology Tianjin 300222 China Institute of Physical Internet Jinan University Zhuhai 519070 China School of Intelligent Systems Science and Engineering Jinan University Zhuhai 519070 China School of Computer & Information Tianjin Chengjian University Tianjin 300384 China

In the process of transporting refrigerated vehicle, the respiration of agricultural products after collection produces CO 2 and other gases, resulting in pollution, deterioration, and waste. This paper uses TDLAS technology to design the CO 2 detection system in refrigerated compartments, which is specially designed to deal with external disturbance factors such as detector noise, optical interference fringes and low temperature in refrigerated compartments. At last, it analyzes several CO 2 data preprocessing methods, for example, starting point alignment, bulky error rejecting, sampling point wavelength corresponding, multicycle average, smooth filtering and eigenvalue extraction. The experiment shows that the data preprocessing method of current tuning in this system can accurately detect CO 2 in refrigerated compartments. The total average relative concentration error is 1.2%, and the root mean square error is 0.07%, which provides a reliable technical support to food quality improve and refrigerated preservation of agricultural products.

关键词： TDLAS agricultural product transportation gas detection CO 2 data preprocessing

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A New Environmental Monitoring System Based on WiFi Technology

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Procedia CIRP 2019年 83卷 394-397页

作者： Rui Zhang Bo Yan HongFei Guo YongHeng Zhang Bin Hu HeXuan Yang LuAn Wang Yan Wang College of Electronic Information and Automation，Tianjin University of Science Technology Tianjin 300222 China Institute of Physical Internet Jinan University Zhuhai 519070 China School of Intelligent Systems Science and Engineering Jinan University Zhuhai 519070 China School of Computer & Information Tianjin Chengjian University Tianjin 300384 China

In order to measure environmental parameters in a better way, we use WiFi technology and design a new environmental monitoring system. This system uses humiture sensor and optical sensor to collect humiture and optical data, through wireless communication module in communication with the host computer. It accepts the collection instruction and sends real-time data to the host computer. This paper shows the structure diagram of system, the circuit diagram of control node and sensor node, the flow chart of programming and the user interface of PC. Generally, this system is easily-operated, rapid, and functional thus has the potential to be widely applicated.

关键词： WiFi MCU sensor environmental monitoring

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